Learning has been used to address a variety of problems for multi-robot systems and is becoming more popular as deep learning tools are made more accessible. A deep learned controller is used to model multi-quadrotor interactions in [1]} and [2]}, and is shown to considerably outperform traditional non-linear controllers. Reinforcement learning can also be a useful tool in multi-robot contexts and [3]} shows that an offline learned value function can be used to perform real-time and uncertainty aware collision avoidance. The collective training methods in [4]} and [5]} demonstrate how experience aggregation from multiple robots can speed up policy optimization. While not deep learning, Gaussian processes are another popular learning tool that have been used for various regression tasks with data collected online by multi-robot systems [6]}, [7]}. These works all showcase applicability of collaborative learning models for multi-robot systems, but in general, aside from [7]}, do not provide a distributed framework from which to perform this learning.
